Among the well-known methods of person identification by facial image is the eigenface technique (U.S. Pat. No. RE36041). This technique is based on image decomposition in an orthonormalized Karhunen-Loeve (KL) basis. The coefficients of the image decomposition are used as an image features vector. Image recognition by the eigenface technique is based on a search of a e0 template from the set E of templates, which has the least distance to the input image f among all the other templates, also presented in KL basis as
      e    0    =                    arg        ⁢                                                  ⁢                                                ⁢        min                              e          k                ⋐                                  ⁢        E              ⁢                                    f          -                      e            k                                      .      
The results of identification by the eigenface technique are unreliable when changes between facial images occur due to lighting conditions, as well as when there are errors in normalization of the facial image that is being recognized.
There is a method of person identification (U.S. Pat. No. 6,801,641) in which 3D models of faces are composed of 3D models of various face elements. These elements represent separate face features. Each of the face element models is given a certain code. A face feature index or vector is a sequence of pairs such as <element code, model code>.
The reason behind preventing achievement of good performance using such 3D models is that a robust three-dimensional reconstruction of objects is done by means of laser rangers, which is prohibitively expensive.
There is a method of person identification by video images (U.S. Pat. No. 6,301,370) in which a template of a person's face in a database is a “generalized face bunch graph” with its nodes corresponding to nodes of a net overlaid over the inputted facial image. Face features are determined for each of these nodes. These features are the informative characteristics of a face around the point corresponding to the graph node. These features represent absolute values of convolutions of the image with 32 two-dimensional Gabor filters. A Gabor filter is a linear filter whose impulse response is defined by a harmonic function multiplied by a Gaussian function. Because of the multiplication-convolution property (Convolution theorem), the Fourier transform of a Gabor filter's impulse response is the convolution of the Fourier transform of the harmonic function and the Fourier transform of the Gaussian function. More than one feature vector may correspond to each of these graph nodes. The feature vectors relate to different variations of the respective image element (e.g., open eyes, closed eyes). By means of an elastic bunch graph matching algorithm, the input image points corresponding to generalized graph nodes are matched. Thereafter, the obtained graph is compared with template graphs from the database.
However, errors in detection of correspondence between nodes of the generalized face bunch graph and singular points on the facial image prevent correct face recognition using the “generalized face bunch graph.” Furthermore, not all components of the feature vectors based on the Gabor filters are informative for the image points. Accordingly, the distance between facial images of different people may decrease enough to result in recognition errors.